Among cognitive modellers, deep learning architectures and algorithms are becoming popular as they represent a new efficient approach to building many layers of information processing stages in deep architectures for pattern classification and for feature or representation learning [1]}. Recent studies, i.e. [2]}, [3]} show that sensory-motor information (finger positions) from the child-robot iCub can increase the efficiency of deep learning models in the recognition of spoken and written digits from real world databases. The results of the simulated training show several similarities with studies in developmental psychology, such as a quicker creation of a uniform number line. A discussion on deep learning models for numerical cognition can be found in [4]}.
